Methods and systems for biodirectional indexing

ABSTRACT

In an aspect, provided is a method comprising receiving a data model, generating a bidirectional table index (BTI) based on the data model, generating a bidirectional association index (BAI) based on the data model and the bidirectional table index, and loading a portion of the data model, the BAI, and the BTI in-memory.

CROSS REFERENCE TO RELATED PATENT APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/487,142, filed Apr. 13, 2017, which claims priority to U.S.Provisional Application No. 62/322,424 filed Apr. 14, 2016, each ofwhich are herein incorporated by reference in their entireties.

SUMMARY

It is to be understood that both the following general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive, as claimed. Provided are methods and systemsfor data management and analysis.

In an aspect, provided is a method comprising receiving a data model,generating a bidirectional table index (BTI) based on the data model,generating a bidirectional association index (BAI) based on the datamodel and the bidirectional table index, and loading a portion of thedata model, the BAI, and the BTI in-memory.

In an aspect, provided is a method comprising determining a binary stateof each field and of each data table of a data source, resulting in astate space, wherein determining the binary state comprises generating abidirectional table index (BTI) and a bidirectional association index(BAI), providing a user interface comprising one or more objectsrepresenting data in the state space, receiving a user selection in theuser interface, recalculating the state space based on the userselection, the BTI, and the BAI, and providing the user interfacecomprising the one or more objects updated according to the state spacebased on the user selection.

In an aspect, provided is a method comprising receiving a user selectionof data, wherein the data comprises one or more tables, determiningdistinct values in all related tables that are relevant to the userselection based on a bidirectional table index (BTI) and a bidirectionalassociation index (BAI), performing a first calculation on the distinctvalues to create a first hypercube, and generating a graphical objectbased on the first hypercube.

Additional advantages will be set forth in part in the description whichfollows or may be learned by practice. The advantages will be realizedand attained by means of the elements and combinations particularlypointed out in the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate embodiments and together with thedescription, serve to explain the principles of the methods and systems:

FIG. 1 is a schematic diagram showing an embodiment of a system formingan implementation of the disclosed methods;

FIG. 2 is a set of tables showing exemplary Tables 1-5 of a simpledatabase and associations between variables in the tables;

FIG. 3 is a schematic flowchart showing basic steps performed whenextracting information from a database;

FIG. 4 is tables showing a final data structure, e.g. a multidimensionalcube, created by evaluating mathematical functions;

FIG. 5A is a schematic diagram showing how a selection by a useroperates on a scope to generate a data subset;

FIG. 5B is an overview of the relations between data model, indexes indisk and windowed view of disk indexes in memory;

FIG. 5C is a representation of the data structure used for tablehandling;

FIG. 5D is a table tree representation of a data model;

FIG. 5E illustrates an example application of bidirectional tableindexes and bidirectional association indexes;

FIG. 6 is a schematic graphical presentation showing selections and adiagram of data associated to the selections as received afterprocessing by an external engine;

FIG. 7 is a schematic representation of data exchanged with an externalengine based on selections in FIG. 6;

FIG. 8 is a schematic graphical presentation showing selections and adiagram of data associated to the selections as received after secondcomputations from an external engine;

FIG. 9 is a schematic representation of data exchanged with an externalengine based on selections in FIG. 8;

FIG. 10 is a schematic graphical presentation showing selections and adiagram of data associated to the selections as received after thirdcomputations from an external engine;

FIG. 11 is a schematic representation of data exchanged with an externalengine based on selections in FIG. 10;

FIG. 12 is a table showing results from computations based on differentselections in the presentation of FIG. 10;

FIG. 13 is a schematic graphical presentation showing a further set ofselections and a diagram of data associated to the selections asreceived after third computations from an external engine;

FIG. 14 is a flow chart illustrating an example method;

FIG. 15 is a flow chart illustrating another example method;

FIG. 16 is a flow chart illustrating another example method; and

FIG. 17 is an exemplary operating environment for performing thedisclosed methods.

DETAILED DESCRIPTION

Before the present methods and systems are disclosed and described inmore detail, it is to be understood that the methods and systems are notlimited to specific steps, processes, components, or structuredescribed, or to the order or particular combination of such steps orcomponents as described. It is also to be understood that theterminology used herein is for the purpose of describing exemplaryembodiments only and is not intended to be restrictive or limiting.

As used herein the singular forms “a,” “an,” and “the” include bothsingular and plural referents unless the context clearly dictatesotherwise. Values expressed as approximations, by use of antecedentssuch as “about” or “approximately,” shall include reasonable variationsfrom the referenced values. If such approximate values are included withranges, not only are the endpoints considered approximations, themagnitude of the range shall also be considered an approximation. Listsare to be considered exemplary and not restricted or limited to theelements comprising the list or to the order in which the elements havebeen listed unless the context clearly dictates otherwise.

Throughout the specification and claims of this disclosure, thefollowing words have the meaning that is set forth: “comprise” andvariations of the word, such as “comprising” and “comprises,” meanincluding but not limited to, and are not intended to exclude, forexample, other additives, components, integers or steps. “Exemplary”means “an example of”, but not essential, necessary, or restricted orlimited to, nor does it convey an indication of a preferred or idealembodiment. “Include” and variations of the word, such as “including”are not intended to mean something that is restricted or limited to whatis indicated as being included, or to exclude what is not indicated.“May” means something that is permissive but not restrictive orlimiting. “Optional” or “optionally” means something that may or may notbe included without changing the result or what is being described.“Prefer” and variations of the word such as “preferred” or “preferably”mean something that is exemplary and more ideal, but not required. “Suchas” means something that is exemplary.

Steps and components described herein as being used to perform thedisclosed methods and construct the disclosed systems are exemplaryunless the context clearly dictates otherwise. It is to be understoodthat when combinations, subsets, interactions, groups, etc. of thesesteps and components are disclosed, that while specific reference ofeach various individual and collective combinations and permutation ofthese may not be explicitly disclosed, each is specifically contemplatedand described herein, for all methods and systems. This applies to allaspects of this application including, but not limited to, steps indisclosed methods and/or the components disclosed in the systems. Thus,if there are a variety of additional steps that can be performed orcomponents that can be added, it is understood that each of theseadditional steps can be performed and components added with any specificembodiment or combination of embodiments of the disclosed systems andmethods.

The present methods and systems may be understood more readily byreference to the following detailed description of preferred embodimentsand the Examples included therein and to the Figures and their previousand following description.

As will be appreciated by one skilled in the art, the methods andsystems may take the form of an entirely hardware embodiment, anentirely software embodiment, or an embodiment combining software andhardware aspects. Furthermore, the methods and systems may take the formof a computer program product on a computer-readable storage mediumhaving computer-readable program instructions (e.g., computer software)embodied in the storage medium. More particularly, the present methodsand systems may take the form of web-implemented computer software. Anysuitable computer-readable storage medium may be utilized including harddisks, CD-ROMs, optical storage devices, or magnetic storage devices,whether internal, networked or cloud based.

Embodiments of the methods and systems are described below withreference to diagrams, flowcharts and other illustrations of methods,systems, apparatuses and computer program products. It will beunderstood that each block of the block diagrams and flowchartillustrations, and combinations of blocks in the block diagrams andflowchart illustrations, respectively, can be implemented by computerprogram instructions. These computer program instructions may be loadedonto a general purpose computer, special purpose computer, or otherprogrammable data processing apparatus to produce a machine, such thatthe instructions which execute on the computer or other programmabledata processing apparatus create a means for implementing the functionsspecified in the flowchart block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including computer-readableinstructions for implementing the function specified in the flowchartblock or blocks. The computer program instructions may also be loadedonto a computer or other programmable data processing apparatus to causea series of operational steps to be performed on the computer or otherprogrammable apparatus to produce a computer-implemented process suchthat the instructions that execute on the computer or other programmableapparatus provide steps for implementing the functions specified in theflowchart block or blocks.

Accordingly, blocks of the block diagrams and flowchart illustrationssupport combinations of means for performing the specified functions,combinations of steps for performing the specified functions and programinstruction means for performing the specified functions. It will alsobe understood that each block of the block diagrams and flowchartillustrations, and combinations of blocks in the block diagrams andflowchart illustrations, can be implemented by special purposehardware-based computer systems that perform the specified functions orsteps, or combinations of special purpose hardware and computerinstructions.

The present disclosure relates to computer implemented methods andsystems for data management, data analysis, and processing. Thedisclosed methods and systems can incorporate external data analysisinto an otherwise closed data analysis environment. A typicalenvironment for the systems and methods described herein is forassisting in a computer implemented method for building and updating amulti-dimensional cube data structure, such as, e.g., the systems andmethods described in U.S. Pat. Nos. 7,058,621; 8,745,099; 8,244,741; andU.S. patent application Ser. No. 14/054,321, which are incorporated byreference in their entireties.

In an aspect, the methods and systems can manage associations among datasets with every data point in the analytic dataset being associated withevery other data point in the dataset. Datasets can be larger thanhundreds of tables with thousands of fields. A multi-dimensional datasetor array of data is referred to as an OnLine Analytic Processing (OLAP)cube. A cube can be considered a multi-dimensional generalization of atwo- or three-dimensional spreadsheet. For example, it may be desired tosummarize financial data by product, by time-period, and by city tocompare actual and budget expenses. Product, time, city, and scenario(actual and budget) can be referred to as dimensions. Amulti-dimensional dataset is normally called a hypercube if the numberof dimensions is greater than 3. A hypercube can comprise tuples made oftwo (or more) dimensions and one or more expressions.

FIG. 1 illustrates an associative data indexing engine 100 with dataflowing in from the left and operations starting from a script engine104 and going clockwise (indicated by the clockwise arrow) to exportfeatures 118. Data from a data source 102 can be extracted by a scriptengine 104. The data source 102 can comprise any type of known database,such as relational databases, post-relational databases, object-orienteddatabases, hierarchical databases, flat files, spread sheet, etc. TheInternet may also be regarded as a database in the context of thepresent disclosure. A visual interface can be used as an alternative orcombined with a script engine 104. The script engine 104 can read recordby record from the data source 102 and data can be stored or appended tosymbol and data tables in an internal database 120. Read data can bereferred to as a data set.

In an aspect, the extraction of the data can comprise extracting aninitial data set or scope from the data source 102, e.g. by reading theinitial data set into the primary memory (e.g. RAM) of the computer. Theinitial data set can comprise the entire contents of the data source 102base, or a subset thereof. The internal database 120 can comprise theextracted data and symbol tables. Symbol tables can be created for eachfield and, in one aspect, can only contain the distinct field values,each of which can be represented by their clear text meaning and a bitfilled pointer. The data tables can contain said bit filled pointers.

In the case of a query of the data source 102, a scope can be defined bythe tables included in a SELECT statement (or equivalent) and how theseare joined. In an aspect, the SELECT statement can be SQL (StructuredQuery Language) based. For an Internet search, the scope can be an indexof found web pages, for example, organized as one or more tables. Aresult of scope definition can be a data set.

Once the data has been extracted, a user interface can be generated tofacilitate dynamic display of the data. By way of example, a particularview of a particular dataset or data subset generated for a user can bereferred to as a state space or a session. The methods and systems candynamically generate one or more visual representations of the data topresent in the state space.

A user can make a selection in the data set, causing a logical inferenceengine 106 to evaluate a number of filters on the data set. For example,a query on a database that holds data of placed orders, could berequesting results matching an order year of ‘1999’ and a client groupbe ‘Nisse.’ The selection may thus be uniquely defined by a list ofincluded fields and, for each field, a list of selected values or, moregenerally, a condition. Based on the selection, the logical inferenceengine 106 can generate a data subset that represents a part of thescope. The data subset may thus contain a set of relevant data recordsfrom the scope, or a list of references (e.g. indices, pointers, orbinary numbers) to these relevant data records. The logical inferenceengine 106 can process the selection and can determine what otherselections are possible based on the current selections. In an aspect,flags can enable the logical inference engine 106 to work out thepossible selections. By way of example, two flags can be used: the firstflag can represent whether a value is selected or not, the second canrepresent whether or not a value selection is possible. For every clickin an application, states and colors for all field values can becalculated. These can be referred to as state vectors, which can allowfor state evaluation propagation between tables.

The logical inference engine 106 can utilize an associative model toconnect data. In the associative model, all the fields in the data modelhave a logical association with every other field in the data model. Anexample, data model 501 is shown in FIG. 5B. The data model 501illustrates connections between a plurality of tables which representlogical associations. Depending on the amount of data, the data model501 can be too large to be loaded into memory. To address this issue,the logical inference engine 106 can generate one or more indexes forthe data model. The one or more indexes can be loaded into memory inlieu of the data model 501. The one or more indexes can be used as theassociative model. An index is used by database management programs toprovide quick and efficient associative access to a table's records. Anindex is a data structure (for example, a B-tree, a hash table, and thelike) that stores attributes (e.g., values) for a specific column in atable. A B-tree is a self-balancing tree data structure that keeps datasorted and allows searches, sequential access, insertions, and deletionsin logarithmic time. The B-tree is a generalization of a binary searchtree in that a node can have more than two children. A hash table (alsoreferred to as a hash index) can comprise a collection of bucketsorganized in an array. A hash function maps index keys to correspondingbuckets in the hash index.

Queries that compare for equality to a string can retrieve values veryfast using a hash index. For instance, referring to the tables of FIG.2, a query of SELECT * FROM Table 2 WHERE Client=‘Kane’) could benefitfrom a hash index created on the Client column. In this example, thehash index would be configured such that the column value will be thekey into the hash index and the actual value mapped to that key wouldjust be a pointer to the row data in Table 2. Since a hash index is anassociative array, a typical entry can comprise “Kalle=>0x29838”, where0x29838 is a reference to the table row where Kalle is stored in memory.Thus, looking up a value of “Kalle” in a hash index can return areference to the row in memory which is faster than scanning Table 2 tofind all rows with a value of “Kalle” in the Client column. The pointerto the row data enables retrieval of other values in the row.

As shown in FIG. 5B, the logical inference engine 106 can be configuredfor generating one or more bidirectional table indexes (BTI) 502 a, 502b, 502 c, 502 d, and/or 502 e and one or more bidirectional associativeindexes (BAI) 503 a, 503 b, 503 c and/or 503 d based on a data model501. The logical inference engine 106 can scan each table in the datamodel 501 and create the BTI 502 a, 502 b, 502 c, 502 d, and/or 502 e. ABTI can be created for each column of each table in the data. The BTI502 a, 502 b, 502 c, 502 d, and/or 502 e can comprise a hash index. TheBTI 502 a, 502 b, 502 c, 502 d, and/or 502 e can comprise firstattributes and pointers to the table rows comprising the firstattributes. For example, referring to the tables of FIG. 2, an exampleBTI 502 a can comprise “Kalle=>0x29838”, where Kalle is an attributefound in Table 2 and 0x29838 is a reference to the row in Table 2 whereKalle is stored in memory. Thus, the BTI 502 a, 502 b, 502 c, 502 d,and/or 502 e can be used to determine other attributes in other columns(e.g., second attributes, third attributes, etc. . . . ) in table rowscomprising the first attributes. Accordingly, the BTI can be used todetermine that an association exists between the first attributes andthe other attributes.

The logical inference engine 106 can scan one or more of BTI 502 a, 502b, 502 c, 502 d, and/or 502 e and create the BAI 503 a, 503 b, 503 cand/or 503 d. The BAI 503 a, 503 b, 503 c and/or 503 d can comprise ahash index. The BAI 503 a, 503 b, 503 c and/or 503 d can comprise anindex configured for connecting attributes in a first table to commoncolumns in a second table. The BAI 503 a, 503 b, 503 c and/or 503 d thusallows for identification of rows in the second table which then permitsidentification of other attributes in other tables. For example,referring to the tables of FIG. 2, an example BAI 503 a can comprise“Kalle=>0x39838”, where Kalle is an attribute found in Table 2 and0x39838 is a reference to a row in Table 4 that contains Kalle. In anaspect, the reference can be to a hash that can be in-memory or on disk.

Using the BTI 502 a, 502 b, 502 c, 502 d, and/or 502 e and the BAI 503a, 503 b, 503 c, and/or 503 d, the logical inference engine 106 cangenerate an index window 504 by taking a portion of the data model 501and mapping it into memory. The portion of the data model 501 taken intomemory can be sequential (e.g., not random). The result is a significantreduction in the size of data required to be loaded into memory.

Thus, the logical inference engine 106 can determine a data subset basedon user selections. The logical inference engine 106 automaticallymaintains associations among every piece of data in the entire data setused in an application. The logical inference engine 106 can store thebinary state of every field and of every data table dependent on userselection (e.g., included or excluded). This can be referred to as astate space and can be updated by the logical inference engine 106 everytime a selection is made. There is one bit in the state space for everyvalue in the symbol table or row in the data table, as such the statespace is smaller than the data itself and faster to query. The inferenceengine will work associating values or binary symbols into the dimensiontuples. Dimension tuples are normally needed by a hypercube to produce aresult.

The associations thus created by the logical inference engine 106 meansthat when a user makes a selection, the logical inference engine 106 canresolve (quickly) which values are still valid (e.g., possible values)and which values are excluded. The user can continue to make selections,clear selections, and make new selections, and the logical inferenceengine 106 will continue to present the correct results from the logicalinference of those selections. In contrast to a traditional join modeldatabase, the associative model provides an interactive associativeexperience to the user.

FIG. 5C, illustrates an example application of one or more BTIs. Userinput 504 can be received that impacts a selection of one or moreattribute states 506. Attribute states 506 can correspond to selectionby a user of one or more attributes (e.g., values) found in Column 1 ofTable X. In an aspect, the one or more attributes of Table X cancomprise a hash of each respective attribute. One or more BTI's 508 canbe accessed to determine one or more rows in Table X that comprise theattributes selected by the user. Row states 510 can correspond toselection of one or more rows found in Table X that comprise the one ormore selected attributes. An inverted index 512 of Column 2 can beaccessed to identify which rows of Table 1 comprise associatedattributes. Attribute states 514 for Column 2 can be updated to reflectthe associated attributes of Column 2. One or more BTI's 518 can befurther accessed to determine other associated attributes in othercolumns as needed. Attribute states 514 can be applied to other tablesvia one or more BAIs. FIG. 5D illustrates an example of relationshipsidentified by one or more BAIs.

FIG. 5E illustrates an example application of BTI's and BAI's todetermine inferred states both inter-table and intra-table using Table 1and Table 2 of FIG. 2. A BTI 520 can be generated for the “Client”attribute of Table 2. In an aspect, the BTI 520 can comprise an invertedindex 521. In other aspect, the inverted index 521 can be considered aseparate structure. The BTI 520 can comprise a row for each uniqueattribute in the “Client” column of Table 2. Each unique attribute canbe assigned a corresponding position 522 in the BTI 520. In an aspect,the BTI 520 can comprise a hash for each unique attribute. The BTI 520can comprise a column 523 for each row of Table 2. For each attribute, a“1” can indicate the presence of the attribute in the row and a “0” canindicate an absence of the attribute from the row. “0” and “1” aremerely examples of values used to indicate presence or absence. Thus,the BTI 520 reflects that the attribute “Nisse” is found in rows 1 and 6of Table 2, the attribute “Gullan” is found in row 2 of Table 2, theattribute “Kalle” is found in rows 3 and 4 of Table 2, and the attribute“Pekka” is found in row 5 of Table 2.

The inverted index 521 can be generated such that each position in theinverted index 521 corresponds to a row of Table 2 (e.g., first positioncorresponds to row 1, second position corresponds to row 2, etc. . . .). A value can be entered into each position that reflects thecorresponding position 522 for each attribute. Thus, in the invertedindex 521, position 1 comprises the value “1” which is the correspondingposition 522 value for the attribute “Nisse”, position 2 comprises thevalue “2” which is the corresponding position 522 value for theattribute “Gullan”, position 3 comprises the value “3” which is thecorresponding position 522 value for the attribute “Kalle”, position 4comprises the value “3” which is the corresponding position 522 valuefor the attribute “Kalle”, position 5 comprises the value “4” which isthe corresponding position 522 value for the attribute “Pekka”, andposition 6 comprises the value “1” which is the corresponding position522 value for the attribute “Nisse”.

A BTI 524 can be generated for the “Product” attribute of Table 2. In anaspect, the BTI 524 can comprise an inverted index 525. In other aspect,the inverted index 525 can be considered a separate structure. The BTI524 can comprise a row for each unique attribute in the “Product” columnof Table 2. Each unique attribute can be assigned a correspondingposition 526 in the BTI 524. In an aspect, the BTI 524 can comprise ahash for each unique attribute. The BTI 524 can comprise a column 527for each row of Table 2. For each attribute, a “1” can indicate thepresence of the attribute in the row and a “0” can indicate an absenceof the attribute from the row. “0” and “1” are merely examples of valuesused to indicate presence or absence. Thus, the BTI 524 reflects thatthe attribute “Toothpaste” is found in row 1 of Table 2, the attribute“Soap” is found in rows 2, 3, and 5 of Table 2, and the attribute“Shampoo” is found in rows 4 and 6 of Table 2.

By way of example, the inverted index 525 can be generated such thateach position in the inverted index 525 corresponds to a row of Table 2(e.g., first position corresponds to row 1, second position correspondsto row 2, etc. . . . ). A value can be entered into each position thatreflects the corresponding position 526 for each attribute. Thus, in theinverted index 525, position 1 comprises the value “1” which is thecorresponding position 526 value for the attribute “Toothpaste”,position 2 comprises the value “2” which is the corresponding position526 value for the attribute “Soap”, position 3 comprises the value “2”which is the corresponding position 526 value for the attribute “Soap”,position 4 comprises the value “3” which is the corresponding position526 value for the attribute “Shampoo”, position 5 comprises the value“2” which is the corresponding position 526 value for the attribute“Soap”, and position 6 comprises the value “3” which is thecorresponding position 526 value for the attribute “Shampoo”.

By way of example, a BTI 528 can be generated for the “Product”attribute of Table 1. In an aspect, the BTI 528 can comprise an invertedindex 529. In other aspect, the inverted index 529 can be considered aseparate structure. The BTI 528 can comprise a row for each uniqueattribute in the “Product” column of Table 1. Each unique attribute canbe assigned a corresponding position 530 in the BTI 528. In an aspect,the BTI 528 can comprise a hash for each unique attribute. The BTI 528can comprise a column 531 for each row of Table 1. For each attribute, a“1” can indicate the presence of the attribute in the row and a “0” canindicate an absence of the attribute from the row. “0” and “1” aremerely examples of values used to indicate presence or absence. Thus,the BTI 528 reflects that the attribute “Soap” is found in row 1 ofTable 1, the attribute “Soft Soap” is found in row 2 of Table 1, and theattribute “Toothpaste” is found in rows 3 and 4 of Table 1.

By way of example, the inverted index 529 can be generated such thateach position in the inverted index 529 corresponds to a row of Table 1(e.g., first position corresponds to row 1, second position correspondsto row 2, etc. . . . ). A value can be entered into each position thatreflects the corresponding position 530 for each attribute. Thus, in theinverted index 529, position 1 comprises the value “1” which is thecorresponding position 530 value for the attribute “Soap”, position 2comprises the value “2” which is the corresponding position 530 valuefor the attribute “Soft Soap”, position 3 comprises the value “3” whichis the corresponding position 530 value for the attribute “Toothpaste”,and position 4 comprises the value “3” which is the correspondingposition 530 value for the attribute “Toothpaste”.

By way of example, a BAI 532 can be generated as an index between theproduct attribute of Table 2 and Table 1. The BAI 532 can comprise a rowfor each unique attribute in the BTI 524 by order of correspondingposition 526. The value in each row can comprise the correspondingposition 530 of the BTI 528. Thus, position 1 of the BAI 532 correspondsto “Toothpaste” in the BTI 524 (corresponding position 526 of 1) andcomprises the value “3” which is the corresponding position 530 for“Toothpaste” of the BTI 528. Position 2 of the BAI 532 corresponds to“Soap” in the BTI 524 (corresponding position 526 of 2) and comprisesthe value “1” which is the corresponding position 530 for “Soap” of theBTI 528. Position 3 of the BAI 532 corresponds to “Shampoo” in the BTI524 (corresponding position 526 of 3) and comprises the value “−1” whichindicates that the attribute “Shampoo” is not found in Table 1.

By way of example, a BAI 533 can be created to create an index betweenthe product attribute of Table 1 and Table 2. The BAI 533 can comprise arow for each unique attribute in the BTI 528 by order of correspondingposition 530. The value in each row can comprise the correspondingposition 526 of the BTI 524. Thus, position 1 of the BAI 533 correspondsto “Soap” in the BTI 528 (corresponding position 530 of 1) and comprisesthe value “2” which is the corresponding position 526 for “Soap” of theBTI 524. Position 2 of the BAI 533 corresponds to “Soft Soap” in the BTI528 (corresponding position 530 of 2) and comprises the value “−1” whichindicates that the attribute “Soft Soap” is not found in Table 2.Position 3 of the BAI 533 corresponds to “Toothpaste” in the BTI 528(corresponding position 530 of 3) and comprises the value “1” which isthe corresponding position 526 for “Toothpaste” of the BTI 524.

FIG. 5E illustrates an example application of the logical inferenceengine 106 utilizing the BTI 520, the BTI 524, and the BTI 528. A usercan select the “Client” “Kalle” from within a user interface. A columnfor a user selection 534 of “Kalle” can be indicated in the BTI 520comprising a value for each attribute that reflects the selection statusof the attribute. Thus, the user selection 534 comprises a value of “0”for the attribute “Nisse” indicating that “Nisse” is not selected, theuser selection 534 comprises a value of “0” for the attribute “Gullan”indicating that “Gullan” is not selected, the user selection 534comprises a value of “1” for the attribute “Kalle” indicating that“Kalle” is selected, and the user selection 534 comprises a value of “0”for the attribute “Pekka” indicating that “Pekka” is not selected.

The BTI 520 can be consulted to determine that the attribute “Kalle” hasa value of “1” in the column 523 corresponding to rows 3 and 4. In anaspect, the inverted index 521 can be consulted to determine that theuser selection 534 relates to the position 522 value of “3” which isfound in the inverted index 521 at positions 3 and 4, implicating rows 3and 4 of Table 1. Following path 535, a row state 536 can be generatedto reflect the user selection 534 as applied to the rows of Table 2. Therow state 536 can comprise a position that corresponds to each row and avalue in each position reflecting whether a row was selected. Thus,position 1 of the row state 536 comprises the value “0” indicating thatrow 1 does not contain “Kalle”, position 2 of the row state 536comprises the value “0” indicating that row 2 does not contain “Kalle”,position 3 of the row state 536 comprises the value “1” indicating thatrow 3 does contain “Kalle”, position 4 of the row state 536 comprisesthe value “1” indicating that row 4 does contain “Kalle”, position 5 ofthe row state 536 comprises the value “0” indicating that row 5 does notcontain “Kalle”, and position 6 of the row state 536 comprises the value“0” indicating that row 6 does not contain “Kalle”.

Following path 537, the row state 536 can be compared with the invertedindex 525 to determine the corresponding position 526 contained in theinverted index 525 at positions 3 and 4. The inverted index 525comprises the corresponding position 526 value of “2” in position 3 andthe corresponding position 526 value of “3” in position 4. Followingpath 538, the corresponding position 526 values of “2” and “3” can bedetermined to correspond to “Soap” and “Shampoo” respectively in the BTI524. Thus, the logical inference engine 106 can determine that both“Soap” and “Shampoo” in Table 2 are associated with “Kalle” in Table 2.The association can be reflected in an inferred state 539 in the BTI524. The inferred state 539 can comprise a column with a row for eachattribute in the BTI 524. The column can comprise a value indicated theselection state for each attribute. The inferred state 539 comprises a“0” for “Toothpaste” indicating that “Toothpaste” is not associated with“Kalle”, the inferred state 539 comprises a “1” for “Soap” indicatingthat “Soap” is associated with “Kalle”, and inferred state 539 comprisesa “1” for “Shampoo” indicating that “Shampoo” is associated with“Kalle”.

Following path 540, the inferred state 539 can be compared to the BAI532 to determine one or more associations between the selection of“Kalle” in Table 2 and one or more attributes in Table 1. As theinferred state 539 comprises a value of “1” in both position 2 andposition 3, the BAI 532 can be assessed to determine the valuescontained in position 2 and position 3 of the BAI 532 (following path541). Position 2 of the BAI 532 comprises the value “1” which identifiesthe corresponding position 530 of “Soap” and position 3 of the BAI 532comprises the value “−1” which indicates that Table 1 does not contain“Shampoo”. Thus, the logical inference engine 106 can determine that“Soap” in Table 1 is associated with “Kalle” in Table 2. The associationcan be reflected in an inferred state 542 in the BTI 528. The inferredstate 542 can comprise a column with a row for each attribute in the BTI528. The column can comprise a value indicated the selection state foreach attribute. The inferred state 542 comprises a “1” for “Soap”indicating that “Soap” is associated with “Kalle”, the inferred state542 comprises a “0” for “Soft Soap” indicating that “Soft Soap” is notassociated with “Kalle”, and the inferred state 542 comprises a “0” for“Toothpaste” indicating that “Toothpaste” is not associated with“Kalle”. Based on the current state of BTIs and BAIs, if the datasources 102 indicate that an update or delta change has occurred to theunderlying data, the BTIs and BAIs can be updated with correspondingchanges to maintain consistency.

Based on current selections and possible rows in data tables acalculation/chart engine 108 can calculate aggregations in objectsforming transient hyper cubes in an application. The calculation/chartengine 108 can further build a virtual temporary table from whichaggregations can be made. The calculation/chart engine 108 can perform acalculation (e.g., evaluate an expression in response to a userselection/de-selection) via a multithreaded operation. The state spacecan be queried to gather all of the combinations of dimensions andvalues necessary to perform the calculation. In an aspect, the query canbe on one thread per object, one process, one worker, combinationsthereof, and the like. The expression can be calculated on multiplethreads per object. Results of the calculation can be passed to arendering engine 116 and/or optionally to an extension engine 110.

Optionally, the extension engine 110 can be implemented to communicatedata via an interface 112 to an external engine 114. In another aspect,the extension engine 110 can communicate data, metadata, a script, areference to one or more artificial neural networks (ANNs), one or morecommands to be executed, one or more expressions to be evaluated,combinations thereof, and the like to the external engine 114. Theinterface 114 can comprise, for example, an Application ProgrammingInterface (API). The external engine 114 can comprise one or more dataprocessing applications (e.g., simulation applications, statisticalapplications, mathematical computation applications, databaseapplications, combinations thereof, and the like). The external engine114 can be, for example, one or more of MATLAB®, R, Maple®,Mathematica®, combinations thereof, and the like.

In an aspect, the external engine 114 can be local to the associativedata indexing engine 100 or the external engine 114 can be remote fromthe associative data indexing engine 100. The external engine 114 canperform additional calculations and transmit the results to theextension engine 110 via the interface 112. A user can make a selectionin the data model of data to be sent to the external engine 114. Thelogical inference engine 106 and/or the extension engine 110 cangenerate data to be output to the external engine 114 in a format towhich the external engine 114 is accustomed to processing. In an exampleapplication, tuples forming a hypercube can comprise two dimensions andone expression, such as (Month, Year, Count (ID)), ID being a recordidentification of one entry. Then said tuples can be exchanged with theexternal engine 114 through the interface 112 as a table. If the datacomprise births there can be timestamps of the births and these can bestored as month and year. If a selection in the data model will give aset of month-year values that are to be sent out to an external unit,the logical inference engine 106 and/or the extension engine 110 canripple that change to the data model associatively and produce the data(e.g., set and/or values) that the external engine 114 needs to workwith. The set and/or values can be exchanged through the interface 112with the external engine 114. The external engine 114 can comprise anymethod and/or system for performing an operation on the set and/orvalues. In an aspect, operations on the set and/or values by theexternal engine 114 can be based on tuples (aggregated or not). In anaspect, operations on the set and/or values by the external engine 114can comprise a database query based on the tuples. Operations on the setand/or values by the external engine 114 can be anytransformation/operation of the data as long as the cardinality of theresult is consonant to the sent tuples/hypercube result.

In an aspect, tuples that are transmitted to the external engine 114through the interface 112 can result in different data being receivedfrom the external engine 114 through the interface 112. For example, atuple consisting of (Month, Year, Count (ID)) should return as 1-to-1,m-to-1 (where aggregations are computed externally) or n-to-n values. Ifdata received are not what were expected, association can be lost.Transformation of data by the external engine 114 can be configured suchthat cardinality of the results is consonant to the sent tuples and/orhypercube results. The amount of values returned can thus preserveassociativity.

Results received by the extension engine 110 from the external engine114 can be appended to the data model. In an aspect, the data can beappended to the data model without intervention of the script engine104. Data model enrichment is thus possible “on the fly.” A natural workflow is available allowing clicking users to associatively extend thedata. The methods and systems disclosed permit incorporation of userimplemented functionality into a presently used work flow. Interactionwith third party complex computation engines, such as MATLAB® or R, isthus facilitated.

The logical inference engine 106 can couple associated results to theexternal engine 114 within the context of an already processed datamodel. The context can comprise tuple or tuples defined by dimensionsand expressions computed by hypercube routines. Association is used fordetermination of which elements of the present data model are relevantfor the computation at hand Feedback from the external engine 114 can beused for further inference inside the inference engine or to providefeedback to the user.

A rendering engine 116 can produce a desired graphical object (charts,tables, etc) based on selections/calculations. When a selection is madeon a rendered object there can be a repetition of the process of movingthrough one or more of the logical inference engine 106, thecalculation/chart engine 108, the extension engine 110, the externalengine 114, and/or the rendering engine 116. The user can explore thescope by making different selections, by clicking on graphical objectsto select variables, which causes the graphical object to change. Atevery time instant during the exploration, there exists a current statespace, which is associated with a current selection state that isoperated on the scope (which always remains the same).

Different export features or tools 118 can be used to publish, export ordeploy any output of the associative data indexing engine 100. Suchoutput can be any form of visual representation, including, but notlimited to, textual, graphical, animation, audio, tactile, and the like.

An example database, as shown in FIG. 2, can comprise a number of datatables (Tables 1-5). Each data table can contain data values of a numberof data variables. For example, in Table 1 each data record containsdata values of the data variables “Product,” “Price,” and “Part.” Ifthere is no specific value in a field of the data record, this field isconsidered to hold a NULL-value. Similarly, in Table 2 each data recordcontains values of the variables “Date,” “Client,” “Product,” and“Number.” In Table 3 each data record contains values of variable “Date”as “Year,” “Month” and “Day.” In Table 4 each data record containsvalues of variables “Client” and “Country,” and in Table 5 each datarecord contains values of variables “Country,” “Capital,” and“Population.” Typically, the data values are stored in the form ofASCII-coded strings, but can be stored in any form.

The methods provided can be implemented by means of a computer programas illustrated in a flowchart of a method 300 in FIG. 3. In a step 302,the program can read some or all data records in the database, forinstance using a SELECT statement which selects all the tables of thedatabase, e.g. Tables 1-5. In an aspect, the database can be read intoprimary memory of a computer.

To increase evaluation speed, each unique value of each data variable insaid database can be assigned a different binary code and the datarecords can be stored in binary-coded form. This can be performed, forexample, when the program first reads the data records from thedatabase. For each input table, the following steps can be carried out.The column names, e.g. the variables, of the table can be read (e.g.,successively). Every time a new data variable appears, a data structurecan be instantiated for the new data variable. An internal tablestructure can be instantiated to contain some or all the data records inbinary form, whereupon the data records can be read (e.g., successively)and binary-coded. For each data value, the data structure of thecorresponding data variable can be checked to establish if the value haspreviously been assigned a binary code. If so, that binary code can beinserted in the proper place in the above-mentioned table structure. Ifnot, the data value can be added to the data structure and assigned anew binary code, for example the next binary code in ascending order,before being inserted in the table structure. In other words, for eachdata variable, a unique binary code can be assigned to each unique datavalue.

After having read some or all data records in the database, the programcan analyze the database in a step 304 to identify all connectionsbetween the data tables. A connection between two data tables means thatthese data tables have one variable in common. In an aspect, step 304can comprise generation of one or more bidirectional table indexes andone or more bidirectional associative indexes. In an aspect, generationof one or more bidirectional table indexes and one or more bidirectionalassociative indexes can comprise a separate step. In another aspect,generation of one or more bidirectional table indexes and one or morebidirectional associative indexes can be on demand. After the analysis,all data tables are virtually connected. In FIG. 2, such virtualconnections are illustrated by double ended arrows. The virtuallyconnected data tables can form at least one so-called “snowflakestructure,” a branching data structure in which there is one and onlyone connecting path between any two data tables in the database. Thus, asnowflake structure does not contain any loops. If loops do occur amongthe virtually connected data tables, e.g. if two tables have more thanone variable in common, a snowflake structure can in some cases still beformed by means of special algorithms known in the art for resolvingsuch loops.

After this initial analysis, the user can explore the database. In doingso, the user defines in a step 306 a mathematical function, which couldbe a combination of mathematical expressions. Assume that the user wantsto extract the total sales per year and client from the database in FIG.2. The user defines a corresponding mathematical function “SUM (x*y)”,and selects the calculation variables to be included in this function:“Price” and “Number.” The user also selects the classificationvariables: “Client” and “Year.”

The computer program then identifies in a step 308 all relevant datatables, e.g. all data tables containing any one of the selectedcalculation and classification variables, such data tables being denotedboundary tables, as well as intermediate data tables in the connectingpath(s) between these boundary tables in the snowflake structure, suchdata tables being denoted connecting tables. There are no connectingtables in the present example. In an aspect, one or more bidirectionaltable indexes and one or more bidirectional associative indexes can beaccessed as part of step 308.

In the present example, all occurrences of every value, e.g. frequencydata, of the selected calculation variables can be included forevaluation of the mathematical function. In FIG. 2, the selectedvariables (“Price,” “Number”) can require such frequency data. Now, asubset (B) can be defined that includes all boundary tables (Tables 1-2)containing such calculation variables and any connecting tables betweensuch boundary tables in the snowflake structure. It should be noted thatthe frequency requirement of a particular variable is determined by themathematical expression in which it is included. Determination of anaverage or a median calls for frequency information. In general, thesame is true for determination of a sum, whereas determination of amaximum or a minimum does not require frequency data of the calculationvariables. It can also be noted that classification variables in generaldo not require frequency data.

Then, a starting table can be selected in a step 310, for example, amongthe data tables within subset (B). In an aspect, the starting table canbe the data table with the largest number of data records in thissubset. In FIG. 2, Table 2 can be selected as the starting table. Thus,the starting table contains selected variables (“Client,” “Number”), andconnecting variables (“Date,” “Product”). These connecting variableslink the starting table (Table 2) to the boundary tables (Tables 1 and3).

Thereafter, a conversion structure can be built in a step 312. Thisconversion structure can be used for translating each value of eachconnecting variable (“Date,” “Product”) in the starting table (Table 2)into a value of a corresponding selected variable (“Year,” “Price”) inthe boundary tables (Table 3 and 1, respectively). A table of theconversion structure can be built by successively reading data recordsof Table 3 and creating a link between each unique value of theconnecting variable (“Date”) and a corresponding value of the selectedvariable (“Year”). It can be noted that there is no link from value 4(“Date: 1999 Jan. 12”), since this value is not included in the boundarytable. Similarly, a further table of the conversion structure can bebuilt by successively reading data records of Table 1 and creating alink between each unique value of the connecting variable (“Product”)and a corresponding value of the selected variable (“Price”). In thisexample, value 2 (“Product: Toothpaste”) is linked to two values of theselected variable (“Price: 6.5”), since this connection occurs twice inthe boundary table. Thus, frequency data can be included in theconversion structure. Also note that there is no link from value 3(“Product: Shampoo”).

When the conversion structure has been built, a virtual data record canbe created. Such a virtual data record accommodates all selectedvariables (“Client,” “Year,” “Price,” “Number”) in the database. Inbuilding the virtual data record, a data record is read in a step 314from the starting table (Table 2). Then, the value of each selectedvariable (“Client”, “Number”) in the current data record of the startingtable can be incorporated in the virtual data record in a step 316.Also, by using the conversion structure each value of each connectingvariable (“Date”, “Product”) in the current data record of the startingtable can be converted into a value of a corresponding selected variable(“Year”, “Price”), this value also being incorporated in the virtualdata record.

In a step 318 the virtual data record can be used to build anintermediate data structure. Each data record of the intermediate datastructure can accommodate each selected classification variable(dimension) and an aggregation field for each mathematical expressionimplied by the mathematical function. The intermediate data structurecan be built based on the values of the selected variables in thevirtual data record. Thus, each mathematical expression can be evaluatedbased on one or more values of one or more relevant calculationvariables in the virtual data record, and the result can be aggregatedin the appropriate aggregation field based on the combination of currentvalues of the classification variables (“Client,” “Year”).

The above procedure can be repeated for one or more additional (e.g.,all) data records of the starting table. In a step 320 it can be checkedwhether the end of the starting table has been reached. If not, theprocess can be repeated from step 314 and further data records can beread from the starting table. Thus, an intermediate data structure canbe built by successively reading data records of the starting table, byincorporating the current values of the selected variables in a virtualdata record, and by evaluating each mathematical expression based on thecontent of the virtual data record. If the current combination of valuesof classification variables in the virtual data record is new, a newdata record can be created in the intermediate data structure to holdthe result of the evaluation. Otherwise, the appropriate data record israpidly found, and the result of the evaluation is aggregated in theaggregation field.

Thus, data records can be added to the intermediate data structure asthe starting table is traversed. The intermediate data structure can bea data table associated with an efficient index system, such as an AVLor a hash structure. The aggregation field can be implemented as asummation register, in which the result of the evaluated mathematicalexpression is accumulated.

In some aspects, e.g. when evaluating a median, the aggregation fieldcan be implemented to hold all individual results for a uniquecombination of values of the specified classification variables. Itshould be noted that only one virtual data record is needed in theprocedure of building the intermediate data structure from the startingtable. Thus, the content of the virtual data record can be updated foreach data record of the starting table. This can minimize the memoryrequirement in executing the computer program.

After traversing the starting table, the intermediate data structure cancontain a plurality of data records. If the intermediate data structureaccommodates more than two classification variables, the intermediatedata structure can, for each eliminated classification variable, containthe evaluated results aggregated over all values of this classificationvariable for each unique combination of values of remainingclassification variables.

When the intermediate data structure has been built, a final datastructure, e.g., a multidimensional cube, as shown in non-binarynotation in Table 6 of FIG. 4, can be created in a step 322 byevaluating the mathematical function (“SUM (x*y)”) based on the resultsof the mathematical expression (“x*y”) contained in the intermediatedata structure. In doing so, the results in the aggregation fields foreach unique combination of values of the classification variables can becombined. In the example, the creation of the final data structure isstraightforward, due to the trivial nature of the present mathematicalfunction. The content of the final data structure can be presented tothe user, for example in a two-dimensional table, in a step 324, asshown in Table 7 of FIG. 4. Alternatively, if the final data structurecontains many dimensions, the data can be presented in a pivot table, inwhich the user can interactively move up and down in dimensions, as iswell known in the art. At step 326, input from the user can be received.For example, input form the user can be a selection and/or de-selectionof the presented results.

Optionally, input from the user at step 326 can comprise a request forexternal processing. In an aspect, the user can be presented with anoption to select one or more external engines to use for the externalprocessing. Optionally, at step 328, data underlying the user selectioncan be configured (e.g., formatted) for use by an external engine.Optionally, at step 330, the data can be transmitted to the externalengine for processing and the processed data can be received. Thereceived data can undergo one or more checks to confirm that thereceived data is in a form that can be appended to the data model. Forexample, one or more of an integrity check, a format check, acardinality check, combinations thereof, and the like. Optionally, atstep 332, processed data can be received from the external engine andcan be appended to the data model as described herein. In an aspect, thereceived data can have a lifespan that controls how long the receiveddata persists with the data model. For example, the received data can beincorporated into the data model in a manner that enables a user toretrieve the received data at another time/session. In another example,the received data can persist only for the current session, making thereceived data unavailable in a future session.

FIG. 5A illustrates how a selection 50 operates on a scope 52 ofpresented data to generate a data subset 54. The data subset 54 can forma state space, which is based on a selection state given by theselection 50. In an aspect, the selection state (or “user state”) can bedefined by a user clicking on list boxes and graphs in a user interfaceof an application. An application can be designed to host a number ofgraphical objects (charts, tables, etc.) that evaluate one or moremathematical functions (also referred to as an “expression”) on the datasubset 54 for one or more dimensions (classification variables). Theresult of this evaluation creates a chart result 56 which can be amultidimensional cube which can be visualized in one or more of thegraphical objects.

The application can permit a user to explore the scope 52 by makingdifferent selections, by clicking on graphical objects to selectvariables, which causes the chart result 56 to change. At every timeinstant during the exploration, there exists a current state space,which can be associated with a current selection state that is operatedon the scope 52 (which always remains the same).

As illustrated in FIG. 5A, when a user makes a selection, the inferenceengine 18 calculates a data subset. Also, an identifier ID1 for theselection together with the scope can be generated based on the filtersin the selection and the scope. Subsequently, an identifier ID2 for thedata subset is generated based on the data subset definition, forexample a bit sequence that defines the content of the data subset. ID2can be put into a cache using ID1 as a lookup identifier. Likewise, thedata subset definition can be put in the cache using ID2 as a lookupidentifier.

As shown in FIG. 5A, a chart calculation in a calculation/chart engine58 takes place in a similar way. Here, there are two information sets:the data subset 54 and relevant chart properties 60. The latter can be,but not restricted to, a mathematical function together with calculationvariables and classification variables (dimensions). Both of theseinformation sets can be used to calculate the chart result 56, and bothof these information sets can be also used to generate identifier ID3for the input to the chart calculation. ID2 can be generated already inthe previous step, and ID3 can be generated as the first step in thechart calculation procedure.

The identifier ID3 can be formed from ID2 and the relevant chartproperties. ID3 can be seen as an identifier for a specific chartgeneration instance, which can include all information needed tocalculate a specific chart result. In addition, a chart resultidentifier ID4 can be created from the chart result definition, forexample a bit sequence that defines the chart result 56. ID4 can be putin the cache using ID3 as a lookup identifier. Likewise, the chartresult definition can be put in the cache using ID4 as a lookupidentifier.

Optionally, further calculations, transforming, and/or processing can beincluded through an extension engine 62. Optionally, associated resultsfrom the inference engine 18 and further computed by hypercubecomputation in said calculation/chart engine 58 can be coupled to anexternal engine 64 that can comprise one or more data processingapplications (e.g., simulation applications, statistical applications,mathematical computation applications, database applications,combinations thereof, and the like). Context of a data model processedby the inference engine 18 can comprise a tuple or tuples of valuesdefined by dimensions and expressions computed by hypercube routines.Data can be exchanged through an interface 66.

The associated results coupled to the external engine 64 can beintermediate.

Further results that can be final hypercube results can also be receivedfrom the external engine 64. Further results can be fed back to beincluded in the Data/Scope 52 and enrich the data model. The furtherresults can also be rendered directly to the user in the chart result56. Data received from and computed by the external engine 64 can beused for further associative discovery.

Each of the data elements of the database shown in Tables 1-5 of FIG. 2has a data element type and a data element value (for example “Client”is the data element type and “Nisse” is the data element value).Multiple records can be stored in different database structures such asdata cubes, data arrays, data strings, flat files, lists, vectors, andthe like; and the number of database structures can be greater than orequal to one and can comprise multiple types and combinations ofdatabase structures. While these and other database structures can beused with, and as part of, the methods and systems disclosed, theremaining description will refer to tables, vectors, strings and datacubes solely for convenience.

Additional database structures can be included within the databaseillustrated as an example herein, with such structures includingadditional information pertinent to the database such as, in the case ofproducts for example; color, optional packages, etc. Each table cancomprise a header row which can identify the various data element types,often referred to as the dimensions or the fields, that are includedwithin the table. Each table can also have one or more additional rowswhich comprise the various records making up the table. Each of the rowscan contain data element values (including null) for the various dataelement types comprising the record.

The database as referred to in Tables 1-5 of FIG. 2 can be queried byspecifying the data element types and data element values of interestand by further specifying any functions to apply to the data containedwithin the specified data element types of the database. The functionswhich can be used within a query can include, for example, expressionsusing statistics, sub-queries, filters, mathematical formulas, and thelike, to help the user to locate and/or calculate the specificinformation wanted from the database. Once located and/or calculated,the results of a query can be displayed to the user with variousvisualization techniques and objects such as list boxes of a userinterface illustrated in FIG. 6.

The graphical objects (or visual representations) can be substantiallyany display or output type including graphs, charts, trees,multi-dimensional depictions, images (computer generated or digitalcaptures), video/audio displays describing the data, hybridpresentations where output is segmented into multiple display areashaving different data analysis in each area and so forth. A user canselect one or more default visual representations; however, a subsequentvisual representation can be generated on the basis of further analysisand subsequent dynamic selection of the most suitable form for the data.

In an aspect, a user can select a data point and a visualizationcomponent can instantaneously filter and re-aggregate other fields andcorresponding visual representations based on the user's selection. Inan aspect, the filtering and re-aggregation can be completed withoutquerying a database. In an aspect, a visual representation can bepresented to a user with color schemes applied meaningfully. Forexample, a user selection can be highlighted in green, datasets relatedto the selection can be highlighted in white, and unrelated data can behighlighted in gray. A meaningful application of a color scheme providesan intuitive navigation interface in the state space.

The result of a standard query can be a smaller subset of the datawithin the database, or a result set, which is comprised of the records,and more specifically, the data element types and data element valueswithin those records, along with any calculated functions, that matchthe specified query. For example, as indicated in FIG. 6, the dataelement value “Nisse” can be specified as a query or filtering criteriaas indicated by a frame in the “Client” header row. In some aspects, theselected element can be highlighted in green. By specifically selecting“Nisse,” other data element values in this row are excluded as shown bygray areas. Further, “Year” “1999” and “Month” “Jan” are selected in asimilar way.

Optionally, in this application, external processing can also berequested by ticking “External” in the user interface of FIG. 6. Data asshown in FIG. 7 can be exchanged with an External engine 64 through theinterface 66 of FIG. 5A. In addition to evaluating the mathematicalfunction (“SUM (Price*Number)”) based on the results of the mathematicalexpression (“Price*Number”) contained in the intermediate data structurethe mathematical function (“SUM (ExtFunc(Price*Number))”) can beevaluated. Data sent out are (Nisse, 1999, Jan, {19.5, null}). In thiscase the external engine 64 can process data in accordance with theformula

-   -   if (x==null)        -   y=0.5    -   else        -   y=x            as shown in in FIG. 7. The result input through the            interface 66 will be (19.5, 0.5) as reflected in the            graphical presentation in FIG. 6.

In a further aspect, external processing can also be optionallyrequested by ticking “External” in a box as shown in FIG. 8. Data asshown in FIG. 9 can be exchanged with an external engine 64 through theInterface 66 of FIG. 5A. In addition to evaluating the mathematicalfunction (“SUM(Price*Number)”) based on the results of the mathematicalexpression (“Price*Number”) contained in the intermediate data structurethe mathematical function

-   -   SUM (ExtFunc(Price*Number))        can be evaluated. Data sent out are (Nisse, 1999, Jan, {19.5,        null}). In this case the external engine 64 will process data in        accordance with Function (1) as shown below and in FIG. 9. The        result input through the Interface 66 will be (61.5) as        reflected in the graphical presentation in FIG. 8.    -   y=ExtAggr(x[ ])        -   for (x in x[ ])            -   if (x==null)                -   y=y+42            -   else                -   y=y+x        -   Function (1)

A further optional embodiment is shown in FIG. 10 and FIG. 11. The samebasic data as in previous examples apply. A user selects “Pekka,”“1999,” “Jan,” and “External.” By selecting “External,” alreadydetermined and associated results are coupled to the external engine 64.Feedback data from the external engine 64 based on an externalcomputation, ExtQualification(Sum(Price*Number)), as shown in FIG. 13will be the information “MVG.” This information can be fed back to thelogical inference engine 18. The information can also be fed back to thegraphical objects of FIG. 10 and as a result a qualification table 68will highlight “MVG” (illustrated with a frame in FIG. 10). Other values(U, G, and VG) are shown in gray areas. The result input through theInterface 66 will be Soap with a value of 75 as reflected in thegraphical presentation (bar chart) of FIG. 10. FIG. 11 is a schematicrepresentation of data exchanged with an external engine based onselections in FIG. 10. FIG. 12 is a table showing results fromcomputations based on different selections in the presentation of FIG.10.

Should a user instead select “Gullan,” “1999,” “Jan,” and “External,”the feedback signal would include “VG” based on the content shown inqualification table 68. The computations actually performed in theexternal engine 62 are not shown or indicated, since they are notrelevant to the inference engine.

In FIG. 13 a user has selected “G” as depicted by 70 in thequalification table 68. As a result information fed back from theexternal engine 64 to the external engine 62 and further to theinference engine 18 the following information will be highlighted:“Nisse,” “1999,” and “Jan” as shown in FIG. 13. Furthermore, the resultproduced will be Soap 37.5 as reflected in the graphical presentation(bar chart) of FIG. 13.

In an aspect, illustrated in FIG. 14 provided is a method 1400comprising receiving a data model at 1402. The data model can comprise aplurality of tables and the plurality of tables each can comprise atleast one row and at least one column.

The method 1400 can comprise generating a bidirectional table index(BTI) based on the data model at 1404. The BTI can comprise a hashindex. The method 1400 can further comprise generating a plurality ofBTIs, wherein a BTI is generated for each column of each of theplurality of tables. Generating the BTI based on the data model cancomprise scanning a first table of the plurality of tables to determinea first attribute in a first column of at least one column of the firsttable, determining a presence or absence of the first attribute in eachrow of at least one row of the first table, assigning the firstattribute a first BTI position in a first BTI, storing the firstattribute at the first BTI position in the first BTI, and for each rowof the at least one row of the first table, storing at the first BTIposition in the first BTI an indication that the first attribute ispresent or absent.

The method 1400 can further comprise scanning the first table of theplurality of tables to determine a second attribute in the first columnof at least one column of the first table, determining a presence orabsence of the second attribute in each row of at least one row of thefirst table, assigning the second attribute a second BTI position in thefirst BTI, storing the second attribute at the second BTI position inthe first BTI, and for each row of the at least one row of the firsttable, storing at the second BTI position in the first BTI an indicationthat the second attribute is present or absent.

The method 1400 can further comprise generating an inverted index.Generating the inverted index can comprise assigning each row of the atleast one row of the first table an inverted index position in theinverted index, wherein the inverted index position assigned correspondsto a number of each row of the at least one row of the first table andstoring in each of the inverted index positions, the first BTI positionof the first BTI if the first attribute is present in the row of the atleast one row of the first table or the second BTI position of the firstBTI if the second attribute is present in the row of the at least onerow of the first table.

The method 1400 can further comprise scanning the first table of theplurality of tables to determine a third attribute in a second column ofat least one column of the first table, determining a presence orabsence of the third attribute in each row of the at least one row ofthe first table, assigning the third attribute a first BTI position in asecond BTI, storing the third attribute at the first BTI position in thesecond BTI, and for each row of the at least one row of the first table,storing at the first BTI position in the second BTI an indication thatthe third attribute is present or absent.

The method 1400 can further comprise scanning the first table of theplurality of tables to determine a fourth attribute in the second columnof the at least one column of the first table, determining a presence orabsence of the fourth attribute in each row of the at least one row ofthe first table, assigning the fourth attribute a second BTI position inthe second BTI, storing the fourth attribute at the second BTI positionin the second BTI, and for each row of the at least one row of the firsttable, storing at the second BTI position in the second BTI anindication that the fourth attribute is present or absent.

The method 1400 can further comprise scanning a second table of theplurality of tables to determine the third attribute in a first columnof at least one column of the second table, determining a presence orabsence of the third attribute in each row of the at least one row ofthe second table, assigning the third attribute a first BTI position ina third BTI, storing the third attribute at the first BTI position inthe third BTI, and for each row of the at least one row of the secondtable, storing at the first BTI position in the third BTI an indicationthat the third attribute is present or absent.

The method 1400 can comprise generating a bidirectional associationindex (BAI) based on the data model and the bidirectional table index at1406. Generating the BAI based on the data model and the BTI cancomprise assigning the third attribute a first BAI position in the BAI,assigning the fourth attribute a second BAI position in the BAI, storingin the first BAI position the first BTI position of the third BTI, andstoring in the second BAI position an indication that the fourthattribute is not found in the third BTI.

In an aspect, the first attribute can comprise a reference to the firstattribute, the second attribute can comprise a reference to the secondattribute, the third attribute can comprise a reference to the thirdattribute, and the fourth attribute can comprise a reference to thefourth attribute. For example, rather than storing the actual attributein a BTI or a BAI, a reference such as a hash can be stored.

The method 1400 can further comprise determining an update to the datamodel, regenerating the BTI based on the updated data model, andregenerating the BAI based on the updated data model and the regeneratedBTI. For example, if the data model is updated to add, delete, or edit atable, the BTI and/or the BAI can be updated to reflect the update.

The method 1400 can comprise loading a portion of the data model, thebidirectional association index (BAI), and the bidirectional table index(BTI) in-memory (e.g., RAM) at 1408. Loading the portion of the datamodel in-memory can comprise sequentially loading the portion of thedata model in-memory.

The method 1400 can further comprise providing a first graphical objectof loaded data based on the data model, wherein the first graphicalobject represents a plurality of data sets, executing a first procedurein an inference engine based on a user selection in the plurality ofdata sets to generate a data subset, wherein the first procedureaccesses the BTI and the BAI, executing a second procedure in acalculation engine to generate a first multidimensional data cube basedon the data subset to generate a second graphical object, and providingthe second graphical object.

In another aspect, illustrated in FIG. 15, provided is a method 1500comprising determining a binary state of each field and of each datatable of a data source, resulting in a state space, wherein determiningthe binary state can comprise generating a bidirectional table index(BTI) and a bidirectional association index (BAI) at 1502.

The method 1500 can further comprise providing a user interfacecomprising one or more objects representing data in the state space at1504. The method 1500 can further comprise receiving a user selection inthe user interface at 1506. The user selection can comprise a firstattribute.

The method 1500 can further comprise recalculating the state space basedon the user selection, the BTI, and the BAI at 1508. Recalculating thestate space based on the user selection can comprise querying the statespace to gather all combinations of dimensions and values to perform therecalculation.

Recalculating the state space based on the user selection, the BTI, andthe BAI can comprise accessing a first BTI of a first table to determinewhich of a plurality of rows of the first table comprise the firstattribute, generating a row state indicating which of the plurality ofrows of the first table comprise the first attribute, comparing the rowstate to an inverted index of a second BTI of the first table todetermine a second attribute, and identifying the second attribute asassociated with the first attribute.

Comparing the row state to the inverted index of the second BTI of thefirst table to determine the second attribute can comprise identifying avalue that indicates a presence of the second attribute in a row of thefirst table in a position of the row state that corresponds to the rowof the first table, determining a value in the inverted index of thesecond BTI in a position of the inverted index of the second BTI thatcorresponds to the position of the row state that corresponds to the rowof the first table, and identifying the second attribute based on thevalue in the inverted index of the second BTI.

The method 1500 can further comprise accessing a BAI to determine whichof a plurality of attributes of a third BTI of a second table areassociated with the second attribute. Accessing the BAI to determinewhich of a plurality of attributes of a third BTI of a second table areassociated with the second attribute can comprise identifying a value ina position of the BAI that corresponds to a position of the secondattribute in the second BTI and identifying an attribute of theplurality of attributes in the third BTI with a position in the thirdBTI that corresponds to the value.

The method 1500 can further comprise updating an inferred state for thesecond attribute in the second BTI and updating an inferred state foreach of the plurality of attributes in the third BTI of the second tablethat are determined to be associated with the second attribute.

The method 1500 can further comprise providing the user interfacecomprising the one or more objects updated according to the state spacebased on the user selection at 1510.

In another aspect, illustrated in FIG. 16, provided is a method 1600comprising receiving a user selection of data, wherein the data cancomprise one or more tables at 1602. The user selection can comprise afirst attribute. In an aspect, all or a portion of the data can residein-memory (e.g., RAM).

The method 1600 can further comprise determining distinct values in allrelated tables that are relevant to the user selection based on abidirectional table index (BTI) and a bidirectional association index(BAI) at 1604.

Determining distinct values in all related tables that are relevant tothe user selection based on the BTI and the BAI can comprise accessing afirst BTI of a first table to determine which of a plurality of rows ofthe first table comprise the first attribute, generating a row stateindicating which of the plurality of rows of the first table comprisethe first attribute, comparing the row state to an inverted index of asecond BTI of the first table to determine a second attribute, andidentifying the second attribute as associated with the first attribute.Comparing the row state to the inverted index of the second BTI of thefirst table to determine the second attribute can comprise identifying avalue that indicates a presence of the second attribute in a row of thefirst table in a position of the row state that corresponds to the rowof the first table, determining a value in the inverted index of thesecond BTI in a position of the inverted index of the second BTI thatcorresponds to the position of the row state that corresponds to the rowof the first table, and identifying the second attribute based on thevalue in the inverted index of the second BTI.

In an aspect, relevant values can comprise values that are active (e.g.,selected and/or indicated as associated). In an aspect, active valuescan comprise values with a “1” as a user selection and/or an inferredstate. In an aspect, relevant values can comprise values that areinactive (e.g., not selected and/or indicated as not associated). In anaspect, inactive values can comprise values with a “0” as a userselection and/or an inferred state. One or more computations can beperformed on inactive values. In an aspect, DeMorgan's theorems can beused to perform one or more computations on inactive values. Forexample, as used in Boolean algebra a variable (e.g., value) canrepresent a logical quantity, for example, 1 or 0. A complement of avariable is an inverse of the variable. One of DeMorgan's theoremsstates that the complement of a product of variables is equal to the sumof the complements of the variable. The other of DeMorgan's theoremsstates that the complement of a sum of variables is equal to the productof the complements of the variables. Thus, in an aspect, the relevantvalues can comprise active values (“1”), inactive values (“0”),combinations thereof, and the like.

The method 1600 can further comprise accessing a BAI to determine whichof a plurality of attributes of a third BTI of a second table areassociated with the second attribute. Accessing the BAI to determinewhich of a plurality of attributes of a third BTI of a second table areassociated with the second attribute can comprise identifying a value ina position of the BAI that corresponds to a position of the secondattribute in the second BTI and identifying an attribute of theplurality of attributes in the third BTI with a position in the thirdBTI that corresponds to the value.

The method 1600 can further comprise performing a first calculation onthe distinct values to create a first hypercube at 1606. The method 1600can further comprise generating a graphical object based on the firsthypercube at 1608.

The method 1600 can further comprise updating an inferred state for thesecond attribute in the second BTI and updating an inferred state foreach of the plurality of attributes in the third BTI of the second tablethat are determined to be associated with the second attribute.

In an exemplary aspect, the methods and systems can be implemented on acomputer 1701 as illustrated in FIG. 17 and described below. Similarly,the methods and systems disclosed can utilize one or more computers toperform one or more functions in one or more locations. FIG. 17 is ablock diagram illustrating an exemplary operating environment forperforming the disclosed methods. This exemplary operating environmentis only an example of an operating environment and is not intended tosuggest any limitation as to the scope of use or functionality ofoperating environment architecture. Neither should the operatingenvironment be interpreted as having any dependency or requirementrelating to any one or combination of components illustrated in theexemplary operating environment.

The present methods and systems can be operational with numerous othergeneral purpose or special purpose computing system environments orconfigurations. Examples of well-known computing systems, environments,and/or configurations that can be suitable for use with the systems andmethods comprise, but are not limited to, personal computers, servercomputers, laptop devices, and multiprocessor systems. Additionalexamples comprise set top boxes, programmable consumer electronics,network PCs, minicomputers, mainframe computers, distributed computingenvironments that comprise any of the above systems or devices, and thelike.

The processing of the disclosed methods and systems can be performed bysoftware components. The disclosed systems and methods can be describedin the general context of computer-executable instructions, such asprogram modules, being executed by one or more computers or otherdevices. Generally, program modules comprise computer code, routines,programs, objects, components, data structures, etc. that performparticular tasks or implement particular abstract data types. Thedisclosed methods can also be practiced in grid-based and distributedcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed computing environment, program modules can be located inboth local and remote computer storage media including memory storagedevices.

Further, one skilled in the art will appreciate that the systems andmethods disclosed herein can be implemented via a general-purposecomputing device in the form of a computer 1701. The components of thecomputer 1701 can comprise, but are not limited to, one or moreprocessors 1703, a system memory 1712, and a system bus 1713 thatcouples various system components including the one or more processors1703 to the system memory 1712. The system can utilize parallelcomputing.

The system bus 1713 represents one or more of several possible types ofbus structures, including a memory bus or memory controller, aperipheral bus, an accelerated graphics port, or local bus using any ofa variety of bus architectures. The bus 1713, and all buses specified inthis description can also be implemented over a wired or wirelessnetwork connection and each of the subsystems, including the one or moreprocessors 1703, a mass storage device 1704, an operating system 1705,associative data indexing engine software 1706, data 1707, a networkadapter 1708, the system memory 1712, an Input/Output Interface 1710, adisplay adapter 1709, a display device 1711, and a human machineinterface 1702, can be contained within one or more remote computingdevices 1714 a,b,c at physically separate locations, connected throughbuses of this form, in effect implementing a fully distributed system.

The computer 1701 typically comprises a variety of computer readablemedia. Exemplary readable media can be any available media that isaccessible by the computer 1701 and comprises, for example and not meantto be limiting, both volatile and non-volatile media, removable andnon-removable media. The system memory 1712 comprises computer readablemedia in the form of volatile memory, such as random access memory(RAM), and/or non-volatile memory, such as read only memory (ROM). Thesystem memory 1712 typically contains data such as the data 1707 and/orprogram modules such as the operating system 1705 and the associativedata indexing engine software 1706 that are immediately accessible toand/or are presently operated on by the one or more processors 1703.

In another aspect, the computer 1701 can also comprise otherremovable/non-removable, volatile/non-volatile computer storage media.By way of example, FIG. 17 illustrates the mass storage device 1704which can provide non-volatile storage of computer code, computerreadable instructions, data structures, program modules, and other datafor the computer 1701. For example and not meant to be limiting, themass storage device 1704 can be a hard disk, a removable magnetic disk,a removable optical disk, magnetic cassettes or other magnetic storagedevices, flash memory cards, CD-ROM, digital versatile disks (DVD) orother optical storage, random access memories (RAM), read only memories(ROM), electrically erasable programmable read-only memory (EEPROM), andthe like.

Optionally, any number of program modules can be stored on the massstorage device 1704, including by way of example, the operating system1705 and the associative data indexing engine software 1706. Each of theoperating system 1705 and the associative data indexing engine software1706 (or some combination thereof) can comprise elements of theprogramming and the associative data indexing engine software 1706. Thedata 1707 can also be stored on the mass storage device 1704. The data1707 can be stored in any of one or more databases known in the art.Examples of such databases comprise, DB2®, Microsoft® Access, Microsoft®SQL Server, Oracle®, mySQL, PostgreSQL, and the like. The databases canbe centralized or distributed across multiple systems.

In an aspect, the associative data indexing engine software 1706 cancomprise one or more of a script engine, a logical inference engine, acalculation engine, an extension engine, and/or a rendering engine. Inan aspect, the associative data indexing engine software 1706 cancomprise an external engine and/or an interface to the external engine.

In another aspect, the user can enter commands and information into thecomputer 1701 via an input device (not shown). Examples of such inputdevices comprise, but are not limited to, a keyboard, pointing device(e.g., a “mouse”), a microphone, a joystick, a scanner, tactile inputdevices such as gloves, and other body coverings, and the like These andother input devices can be connected to the one or more processors 1703via the human machine interface 1702 that is coupled to the system bus1713, but can be connected by other interface and bus structures, suchas a parallel port, game port, an IEEE 1394 Port (also known as aFirewire port), a serial port, or a universal serial bus (USB).

In yet another aspect, the display device 1711 can also be connected tothe system bus 1713 via an interface, such as the display adapter 1709.It is contemplated that the computer 1701 can have more than one displayadapter 1709 and the computer 1701 can have more than one display device1711. For example, the display device 1711 can be a monitor, an LCD(Liquid Crystal Display), or a projector. In addition to the displaydevice 1711, other output peripheral devices can comprise componentssuch as speakers (not shown) and a printer (not shown) which can beconnected to the computer 1701 via the Input/Output Interface 1710. Anystep and/or result of the methods can be output in any form to an outputdevice. Such output can be any form of visual representation, including,but not limited to, textual, graphical, animation, audio, tactile, andthe like. The display device 1711 and computer 1701 can be part of onedevice, or separate devices.

The computer 1701 can operate in a networked environment using logicalconnections to one or more remote computing devices 1714 a,b,c. By wayof example, a remote computing device can be a personal computer,portable computer, smartphone, a server, a router, a network computer, apeer device or other common network node, and so on. Logical connectionsbetween the computer 1701 and a remote computing device 1714 a,b,c canbe made via a network 1715, such as a local area network (LAN) and/or ageneral wide area network (WAN). Such network connections can be throughthe network adapter 1708. The network adapter 1708 can be implemented inboth wired and wireless environments. Such networking environments areconventional and commonplace in dwellings, offices, enterprise-widecomputer networks, intranets, and the Internet. In an aspect, one ormore of the remote computing devices 1714 a,b,c can comprise an externalengine and/or an interface to the external engine.

For purposes of illustration, application programs and other executableprogram components such as the operating system 1705 are illustratedherein as discrete blocks, although it is recognized that such programsand components reside at various times in different storage componentsof the computing device 1701, and are executed by the one or moreprocessors 1703 of the computer. An implementation of the associativedata indexing engine software 1706 can be stored on or transmittedacross some form of computer readable media. Any of the disclosedmethods can be performed by computer readable instructions embodied oncomputer readable media. Computer readable media can be any availablemedia that can be accessed by a computer. By way of example and notmeant to be limiting, computer readable media can comprise “computerstorage media” and “communications media.” “Computer storage media”comprise volatile and non-volatile, removable and non-removable mediaimplemented in any methods or technology for storage of information suchas computer readable instructions, data structures, program modules, orother data. Exemplary computer storage media comprises, but is notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store thedesired information and which can be accessed by a computer.

The methods and systems can employ Artificial Intelligence techniquessuch as machine learning and iterative learning. Examples of suchtechniques include, but are not limited to, expert systems, case basedreasoning, Bayesian networks, behavior based AI, neural networks, fuzzysystems, evolutionary computation (e.g. genetic algorithms), swarmintelligence (e.g. ant algorithms), and hybrid intelligent systems (e.g.Expert inference rules generated through a neural network or productionrules from statistical learning).

While the methods and systems have been described in connection withpreferred embodiments and specific examples, it is not intended that thescope be limited to the particular embodiments set forth, as theembodiments herein are intended in all respects to be illustrativerather than restrictive.

Unless otherwise expressly stated, it is in no way intended that anymethod set forth herein be construed as requiring that its steps beperformed in a specific order. Accordingly, where a method claim doesnot actually recite an order to be followed by its steps or it is nototherwise specifically stated in the claims or descriptions that thesteps are to be limited to a specific order, it is in no way intendedthat an order be inferred, in any respect. This holds for any possiblenon-express basis for interpretation, including: matters of logic withrespect to arrangement of steps or operational flow; plain meaningderived from grammatical organization or punctuation; the number or typeof embodiments described in the specification.

It will be apparent to those skilled in the art that variousmodifications and variations can be made without departing from thescope or spirit. Other embodiments will be apparent to those skilled inthe art from consideration of the specification and practice disclosedherein. It is intended that the specification and examples be consideredas exemplary only, with a true scope and spirit being indicated by thefollowing claims.

1. A method comprising: receiving a data model; generating, based on the data model, a first bidirectional table index (BTI), a second BTI, and a third BTI; generating a bidirectional association index (BAI) based on the data model, the second BTI, and the third BTI, wherein a number identifying a position of the BAI corresponds to a row number of the second BTI; and loading a portion of the data model, the BAI, and at least one of the first BTI, the second BTI, or the third BTI in-memory.
 2. The method of claim 1, wherein a value at the position of the BAI corresponds to a row number of the third BTI.
 3. The method of claim 1, wherein one or more of the first BTI, the second BTI, or the third BTI comprises a hash index.
 4. The method of claim 1, wherein the data model comprises a plurality of tables, wherein each table of the plurality of tables comprises at least one row and at least one column.
 5. The method of claim 4, further comprising generating a plurality of BTIs, wherein a BTI is generated for each column of each of the plurality of tables.
 6. The method of claim 1, further comprising: determining an update to the data model; regenerating one or more of the first BTI, the second BTI, or the third BTI based on the updated data model; and regenerating the BAI based on the updated data model and the regenerated one or more of the first BTI, the second BTI, or the third BTI.
 7. The method of claim 1, wherein loading the portion of the data model in-memory comprises sequentially loading the portion of the data model in-memory.
 8. A method comprising: generating a first bidirectional table index (BTI), a second BTI, a third BTI, and a bidirectional association index (BAI), wherein a number identifying a position of the BAI corresponds to a row number of the second BTI; recalculating a state space based on a user selection in a user interface, the BAI, and one or more of the first BTI, the second BTI, or the third BTI, wherein the user interface comprises one or more objects representing data in the state space; and providing an updated version of the user interface comprising one or more objects updated according to the state space based on the user selection.
 9. The method of claim 8, wherein recalculating the state space comprises querying the state space to gather all combinations of dimensions and values to perform the recalculation.
 10. The method of claim 8, wherein the user selection comprises a first attribute.
 11. The method of claim 10, wherein recalculating the state space based on the user selection comprises: accessing the first BTI to determine which of a plurality of rows of a first table comprise the first attribute, wherein the first BTI is associated with the first table; generating a row state indicating which of the plurality of rows of the first table comprise the first attribute; comparing the row state to an inverted index of the second BTI to determine a second attribute, wherein the second BTI is associated with the first table; and identifying the second attribute as associated with the first attribute.
 12. The method of claim 8, wherein a value at the position of the BAI corresponds to a row number of the third BTI.
 13. The method of claim 8, further comprising determining a binary state of each field and of each data table of a data source, resulting in the state space.
 14. The method of claim 13, wherein determining the binary state comprises generating the first BTI, the second BTI, the third BTI, and the BAI.
 15. A method comprising: receiving a user selection of data, wherein the data comprises one or more tables; determining, based on a user selection of data, distinct values in one or more tables of the data using a bidirectional association index (BAI) and one or more of a first bidirectional table index (BTI), a second BTI, or a third BTI, wherein a number identifying a position of the BAI corresponds to a row number of the second BTI; performing a first calculation on the distinct values; and generating a graphical object based on the first calculation.
 16. The method of claim 15, wherein performing the first calculation on the distinct values results in a hypercube.
 17. The method of claim 16, wherein the graphical object comprises the hypercube.
 18. The method of claim 15, wherein a value at the position of the BAI corresponds to a row number of the third BTI.
 19. The method of claim 15, wherein the user selection comprises a first attribute.
 20. The method of claim 19, wherein determining, based on the user selection of data, the distinct values in the one or more tables of the data using the BAI and one or more of the first the BTI, the second BTI, or the third BTI comprises: accessing the first BTI to determine which of a plurality of rows of a first table comprise the first attribute, wherein the first BTI is associated with the first table; generating a row state indicating which of the plurality of rows of the first table comprise the first attribute; comparing the row state to an inverted index of the second BTI to determine a second attribute, wherein the second BTI is associated with the first table; and identifying the second attribute as associated with the first attribute. 